Applications of Pulsed Laser Ablation in Li-ion Battery Research
Bibliographic record
Abstract
Harnessing pulsed laser ablation processes in the manufacturing of energy storage devices is a new and promising strategy for the facile development of next-generation Li-ion batteries. In laser ablation, a pulsed laser is focused on a material surface such that the transfer of energy causes the removal of localized material via high throughput and environmentally-friendly processing. This chapter will provide a summary of the recent advances in laser ablation technologies for producing Li-ion battery materials and components. In terms of electrode optimization, it will examine the use of pulsed lasers to: (1) generate large specific surface area nanoparticles of active materials or stable integrative anodes; (2) deposit compositionally complex and stoichiometric thin film active materials; (3) create electrode architectures with increased Li-ion diffusion kinetics, enhanced wettability or free space to accommodate Si anode volume expansions, and; (4) remove the superficial inactive or solid electrolyte interface layers from electrode surfaces. It will also investigate the laser ablation of current collectors to produce textures with improved adhesion and the use of pulsed lasers for cutting and structuring solid ceramic electrolyte. Finally, this chapter will discuss the application of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) for chemical composition analysis of Li-ion batteries throughout their operating cycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".